import random
from pathlib import Path
import soundfile as sf
import torch
import torch.utils.data
import tqdm
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class Compose:
"""Composes several augmentation transforms.
Args:
augmentations: list of augmentations to compose.
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, audio):
for transform in self.transforms:
audio = transform(audio)
return audio
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def augment_gain(audio, low=0.25, high=1.25):
"""Applies a random gain to each source between `low` and `high`"""
gain = low + torch.rand(1) * (high - low)
return audio * gain
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def augment_channelswap(audio):
"""Randomly swap channels of stereo sources"""
if audio.shape[0] == 2 and torch.FloatTensor(1).uniform_() < 0.5:
return torch.flip(audio, [0])
return audio
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class MUSDB18Dataset(torch.utils.data.Dataset):
"""MUSDB18 music separation dataset
The dataset consists of 150 full lengths music tracks (~10h duration) of
different genres along with their isolated stems:
`drums`, `bass`, `vocals` and `others`.
Out-of-the-box, asteroid does only support MUSDB18-HQ which comes as
uncompressed WAV files. To use the MUSDB18, please convert it to WAV first:
- MUSDB18 HQ: https://zenodo.org/record/3338373
- MUSDB18 https://zenodo.org/record/1117372
.. note::
The datasets are hosted on Zenodo and require that users
request access, since the tracks can only be used for academic purposes.
We manually check this requests.
This dataset asssumes music tracks in (sub)folders where each folder
has a fixed number of sources (defaults to 4). For each track, a list
of `sources` and a common `suffix` can be specified.
A linear mix is performed on the fly by summing up the sources
Due to the fact that all tracks comprise the exact same set
of sources, random track mixing can be used can be used,
where sources from different tracks are mixed together.
Folder Structure:
>>> #train/1/vocals.wav ---------|
>>> #train/1/drums.wav ----------+--> input (mix), output[target]
>>> #train/1/bass.wav -----------|
>>> #train/1/other.wav ---------/
Args:
root (str): Root path of dataset
sources (:obj:`list` of :obj:`str`, optional): List of source names
that composes the mixture.
Defaults to MUSDB18 4 stem scenario: `vocals`, `drums`, `bass`, `other`.
targets (list or None, optional): List of source names to be used as
targets. If None, a dict with the 4 stems is returned.
If e.g [`vocals`, `drums`], a tensor with stacked `vocals` and
`drums` is returned instead of a dict. Defaults to None.
suffix (str, optional): Filename suffix, defaults to `.wav`.
split (str, optional): Dataset subfolder, defaults to `train`.
subset (:obj:`list` of :obj:`str`, optional): Selects a specific of
list of tracks to be loaded, defaults to `None` (loads all tracks).
segment (float, optional): Duration of segments in seconds,
defaults to ``None`` which loads the full-length audio tracks.
samples_per_track (int, optional):
Number of samples yielded from each track, can be used to increase
dataset size, defaults to `1`.
random_segments (boolean, optional): Enables random offset for track segments.
random_track_mix boolean: enables mixing of random sources from
different tracks to assemble mix.
source_augmentations (:obj:`list` of :obj:`callable`): list of augmentation
function names, defaults to no-op augmentations (input = output)
sample_rate (int, optional): Samplerate of files in dataset.
Attributes:
root (str): Root path of dataset
sources (:obj:`list` of :obj:`str`, optional): List of source names.
Defaults to MUSDB18 4 stem scenario: `vocals`, `drums`, `bass`, `other`.
suffix (str, optional): Filename suffix, defaults to `.wav`.
split (str, optional): Dataset subfolder, defaults to `train`.
subset (:obj:`list` of :obj:`str`, optional): Selects a specific of
list of tracks to be loaded, defaults to `None` (loads all tracks).
segment (float, optional): Duration of segments in seconds,
defaults to ``None`` which loads the full-length audio tracks.
samples_per_track (int, optional):
Number of samples yielded from each track, can be used to increase
dataset size, defaults to `1`.
random_segments (boolean, optional): Enables random offset for track segments.
random_track_mix boolean: enables mixing of random sources from
different tracks to assemble mix.
source_augmentations (:obj:`list` of :obj:`callable`): list of augmentation
function names, defaults to no-op augmentations (input = output)
sample_rate (int, optional): Samplerate of files in dataset.
tracks (:obj:`list` of :obj:`Dict`): List of track metadata
References
"The 2018 Signal Separation Evaluation Campaign" Stoter et al. 2018.
Notes:
This is a modified versions of the MUSDB18 dataset from the version
in Asteroid. It extend the Asteroid version to allow for targets
```vocals``` and ```background```. The background is the sum of all
sources except vocals. This is useful for training models that
separate vocals from background.
"""
dataset_name = "MUSDB18"
def __init__(
self,
root: str,
sources=None,
targets=None,
mix_background=False,
suffix=".wav",
split="train",
subset=None,
exclude_tracks=None,
segment=None,
samples_per_track=1,
random_segments=False,
random_track_mix=False,
source_augmentations=lambda audio: audio,
sample_rate=44100,
):
if sources is None:
sources = ["vocals", "bass", "drums", "other"]
self.root: Path = Path(root).expanduser()
self.split = split
self.sample_rate = sample_rate
self.segment = segment
self.random_track_mix = random_track_mix
self.random_segments = random_segments
self.source_augmentations = source_augmentations
self.sources = sources
self.targets = targets
self.mix_background = mix_background
self.suffix = suffix
self.subset = subset
self.samples_per_track = samples_per_track
if exclude_tracks is None:
exclude_tracks = []
self.tracks = [
x for x in list(self.get_tracks()) if x["path"].stem not in exclude_tracks
]
if not self.tracks:
raise RuntimeError("No tracks found.")
def __getitem__(self, index):
# assemble the mixture of target and interferers
audio_sources = {}
# get track_id
track_id = index // self.samples_per_track
if self.random_segments:
start = random.uniform(
0, self.tracks[track_id]["min_duration"] - self.segment
)
else:
start = 0
self.mixture_path = self.tracks[track_id]["path"]
# load sources
for source in self.sources:
# optionally select a random track for each source
if self.random_track_mix:
# load a different track
track_id = random.choice(range(len(self.tracks)))
if self.random_segments:
start = random.uniform(
0, self.tracks[track_id]["min_duration"] - self.segment
)
# loads the full track duration
start_sample = int(start * self.sample_rate)
# check if dur is none
if self.segment:
# stop in soundfile is calc in samples, not seconds
stop_sample = start_sample + int(self.segment * self.sample_rate)
else:
# set to None for reading complete file
stop_sample = None
# load actual audio
audio, _ = sf.read(
Path(self.tracks[track_id]["path"] / source).with_suffix(self.suffix),
always_2d=True,
start=start_sample,
stop=stop_sample,
)
# convert to torch tensor
audio = torch.tensor(audio.T, dtype=torch.float)
# apply source-wise augmentations
audio = self.source_augmentations(audio)
if self.mix_background:
if source == "vocals":
audio_sources["vocals"] = audio
else:
if "background" not in audio_sources:
audio_sources["background"] = audio
else:
audio_sources["background"] += audio
else:
audio_sources[source] = audio
# apply linear mix over source index=0
audio_mix = torch.stack(list(audio_sources.values())).sum(0)
if self.mix_background:
audio_sources = torch.stack(
[audio_sources["vocals"], audio_sources["background"]],
dim=0,
)
elif self.targets:
audio_sources = torch.stack(
[wav for src, wav in audio_sources.items() if src in self.targets],
dim=0,
)
return audio_mix, audio_sources
def __len__(self):
return len(self.tracks) * self.samples_per_track
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def get_tracks(self):
"""Loads input and output tracks"""
p = Path(self.root, self.split)
for track_path in tqdm.tqdm(p.iterdir()):
if track_path.is_dir():
if self.subset and track_path.stem not in self.subset:
# skip this track
continue
source_paths = [track_path / (s + self.suffix) for s in self.sources]
if not all(sp.exists() for sp in source_paths):
print("Exclude track due to non-existing source", track_path)
continue
# get metadata
infos = list(map(sf.info, source_paths))
if not all(i.samplerate == self.sample_rate for i in infos):
print("Exclude track due to different sample rate ", track_path)
continue
if self.segment is not None:
# get minimum duration of track
min_duration = min(i.duration for i in infos)
if min_duration > self.segment:
yield ({"path": track_path, "min_duration": min_duration})
else:
yield ({"path": track_path, "min_duration": None})
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def get_infos(self):
"""Get dataset infos (for publishing models).
Returns:
dict, dataset infos with keys `dataset`, `task` and `licences`.
"""
infos = dict()
infos["dataset"] = self.dataset_name
infos["task"] = "enhancement"
infos["licenses"] = [musdb_license]
return infos
musdb_license: dict = dict()